If you are running a 1-person software business, micro-SaaS, or content engine, you probably spend at least 2 to 3 hours every day doing repetitive data plumbing:

  • Scanning Hacker News, GitHub Trending, and industry feeds for signal
  • Writing quick bullet summaries or extracting core facts
  • Formatting posts for X, Threads, or sending structured records to your internal database

Most founders start with Zapier, only to immediately hit strict paywalls for multi-step logic, custom webhooks, and routing. At $73.50+/mo, Zapier becomes a heavy tax on early-stage projects.

We replaced that entire stack with Make.com and an optimized 6-module scenario that runs 24/7 without human intervention.

To help fellow builders, we are open-sourcing our exact production scenario blueprint (.json). You can import it in 60 seconds.

🎁 Special Builder Perk: 10,000 Free Operations + 1-Month Core Plan

If you don't already have an active Make.com workspace, you can register via our official developer partner link to unlock an exclusive perk package:

👉 Unlock 10,000 Free Operations & 1-Month Core Plan on Make.com

  • 10,000 Operations Bonus: Enough to run this syndication pipeline hourly for 2 to 3 months without paying a single dollar.
  • 1-Month Core/Pro Access: Unlocks 1-minute scheduling intervals and multi-path routing.
  • Zero Credit Card Trap: You can test and deploy the pipeline immediately.

🏗️ The 6-Node Architecture

The blueprint implements a fault-tolerant, modular data pipeline:

[Module 1: RSS Feed] │ ▼ [Module 2: OpenAI gpt-4o-mini (Strict JSON Mode)] │ ▼ [Module 3: JSON Parser & Sanitizer] │ ▼ [Module 4: Logical Router] ┌┴───────────────────────────┐ ▼ ▼ [Module 5: HTTP Webhook] [Module 6: Social Dispatch] (Archive / Notion / CMS) (X / Threads / Buffer)

Module Breakdown:

  1. Module 1 (rss:ActionReadArticles): Monitored RSS source (default: Hacker News frontpage, customizable to GitHub Trending or TechCrunch). Tracks new items and prevents duplicate execution.
  2. Module 2 (openai:createChatCompletion): Uses gpt-4o-mini with strict system prompting. It extracts 3 non-negotiable core facts and 1 actionable builder insight. It guarantees JSON output formatting.
  3. Module 3 (json:ParseJSON): Parses the raw string returned by OpenAI into native typed Make variables, ensuring subsequent modules never fail due to formatting glitches.
  4. Module 4 (util:Router): Splits the execution into parallel branches. If a social dispatch fails, the database ingestion branch still succeeds independently.
  5. Module 5 (http:ActionSendRequest): Dispatches the structured JSON payload to your custom backend, webhook, Notion database, or Supabase table.
  6. Module 6 (http:ActionSendRequest): Formats high-engagement social copy and posts to X/Twitter v2 API, Buffer, or your scheduling queue.

📄 The Complete Make.com Scenario Blueprint (JSON)

Copy the JSON payload below, save it as make_syndication_blueprint.json, or copy the snippet directly:

{ "name": "Solopreneur News & Social Syndication Engine", "version": 1, "modules": [ { "id": 1, "module": "rss:ActionReadArticles", "version": 1, "parameters": { "url": "https://news.ycombinator.com/rss", "maximumNumberOfResults": 3 }, "metadata": { "designer": { "x": 100, "y": 300 }, "restore": {} } }, { "id": 2, "module": "openai:createChatCompletion", "version": 1, "parameters": { "model": "gpt-4o-mini", "messages": [ { "role": "system", "content": "You are a senior tech analyst. Extract exactly 3 core facts and 1 solopreneur actionable insight. Output STRICT JSON: {\"title\": \"...\", \"summary\": \"...\", \"core_facts\": [\"...\", \"...\", \"...\"], \"builder_insight\": \"...\", \"social_hook\": \"...\"}" }, { "role": "user", "content": "Title: {{1.title}}\nContent: {{1.description}}\nURL: {{1.url}}" } ], "response_format": { "type": "json_object" } }, "metadata": { "designer": { "x": 350, "y": 300 } } }, { "id": 3, "module": "json:ParseJSON", "version": 1, "parameters": { "type": "", "json": "{{2.choices[].message.content}}" }, "metadata": { "designer": { "x": 600, "y": 300 } } }, { "id": 4, "module": "util:Router", "version": 1, "metadata": { "designer": { "x": 850, "y": 300 } } }, { "id": 5, "module": "http:ActionSendRequest", "version": 3, "parameters": { "url": "https://api.yourdomain.com/v1/ingest", "method": "POST", "headers": [ { "name": "Content-Type", "value": "application/json" } ], "body": "{\"title\": \"{{3.title}}\", \"facts\": {{3.core_facts}}, \"insight\": \"{{3.builder_insight}}\", \"url\": \"{{1.url}}\"}" }, "metadata": { "designer": { "x": 1100, "y": 180 } } }, { "id": 6, "module": "http:ActionSendRequest", "version": 3, "parameters": { "url": "https://api.twitter.com/2/tweets", "method": "POST", "headers": [ { "name": "Authorization", "value": "Bearer YOUR_TWITTER_BEARER_TOKEN" }, { "name": "Content-Type", "value": "application/json" } ], "body": "{\"text\": \"{{3.social_hook}}\n\n3 Key Facts:\n1. {{3.core_facts[1]}}\n2. {{3.core_facts[2]}}\n3. {{3.core_facts[3]}}\n\nSource: {{1.url}}\"}" }, "metadata": { "designer": { "x": 1100, "y": 420 } } } ] }

⚡ How to Import in 60 Seconds

  1. Head over to Make.com (claim your 10,000 bonus operations if creating a new account).
  2. Click Create a new scenario in the top right.
  3. In the empty canvas editor, click the "..." (More options) button on the bottom toolbar.
  4. Select Import Blueprint and paste the JSON above or upload the saved .json file.
  5. All 6 nodes will instantly appear wired together on your canvas.
  6. Connect your OpenAI API key in Node 2, customize your webhook in Node 5, and click Run Once to test.
  7. Turn the scenario ON and set scheduling to Every 60 minutes.

💡 Key Lessons From Running This in Production

  • Always use strict JSON mode: LLMs occasionally hallucinate conversational fluff like "Sure, here are your 3 facts:". Using response_format: {"type": "json_object"} completely eliminates downstream parsing errors.
  • Keep routing isolated: Placing the Router before your external API calls ensures that third-party rate limits (e.g. X API limits) won't prevent your database from capturing the day's intelligence.
  • Token cost optimization: By running gpt-4o-mini, processing 50 articles costs less than $0.03 total, making the entire operation practically free.

Give this blueprint a spin in your workflow. If you build cool modifications on top of it, drop a comment below!